{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/bob\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "home_folder = os.path.expanduser(\"~\")\n",
    "print(home_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/bob/Data/Ionosphere/ionosphere.data\n"
     ]
    }
   ],
   "source": [
    "# Change this to the location of your dataset\n",
    "data_folder = os.path.join(home_folder, \"Data\", \"Ionosphere\")\n",
    "data_filename = os.path.join(data_folder, \"ionosphere.data\")\n",
    "print(data_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "\n",
    "# Size taken from the dataset and is known\n",
    "X = np.zeros((351, 34), dtype='float')\n",
    "y = np.zeros((351,), dtype='bool')\n",
    "\n",
    "with open(data_filename, 'r') as input_file:\n",
    "    reader = csv.reader(input_file)\n",
    "    for i, row in enumerate(reader):\n",
    "        # Get the data, converting each item to a float\n",
    "        data = [float(datum) for datum in row[:-1]]\n",
    "        # Set the appropriate row in our dataset\n",
    "        X[i] = data\n",
    "        # 1 if the class is 'g', 0 otherwise\n",
    "        y[i] = row[-1] == 'g'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 263 samples in the training dataset\n",
      "There are 88 samples in the testing dataset\n",
      "Each sample has 34 features\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=14)\n",
    "print(\"There are {} samples in the training dataset\".format(X_train.shape[0]))\n",
    "print(\"There are {} samples in the testing dataset\".format(X_test.shape[0]))\n",
    "print(\"Each sample has {} features\".format(X_train.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "estimator = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_neighbors=5, p=2, weights='uniform')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy is 86.4%\n"
     ]
    }
   ],
   "source": [
    "y_predicted = estimator.predict(X_test)\n",
    "accuracy = np.mean(y_test == y_predicted) * 100\n",
    "print(\"The accuracy is {0:.1f}%\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average accuracy is 82.3%\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(estimator, X, y, scoring='accuracy')\n",
    "average_accuracy = np.mean(scores) * 100\n",
    "print(\"The average accuracy is {0:.1f}%\".format(average_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "avg_scores = []\n",
    "all_scores = []\n",
    "parameter_values = list(range(1, 21))  # Including 20\n",
    "for n_neighbors in parameter_values:\n",
    "    estimator = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
    "    scores = cross_val_score(estimator, X, y, scoring='accuracy')\n",
    "    avg_scores.append(np.mean(scores))\n",
    "    all_scores.append(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.plot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f58cf956ac8>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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38t0HAAAAAADAcpKr8yUXGECr7ETnxunvo1M1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\nRsnY2c77/0DoVAAwTHXV+fiyAAAAgFpqzTPJ/JIAM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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f58cf990e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.figure(figsize=(32,20))\n",
    "plt.plot(parameter_values, avg_scores, '-o', linewidth=5, markersize=24)\n",
    "#plt.axis([0, max(parameter_values), 0, 1.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SFMGP2F4ETqTcliK4FzMUgf/33ve4r0QzD7wCPE2Ghx0yzMJ8Y/oWI74/0wz2\nyq1P3scxb4RjwO9gvkpLMrroPRvXfwd+FjMk8Tbwx+k2J3cOYErTfA641nfb0PdnmsE+yg+2ZDRv\n+3/DP2KT8f095usxwE8DboptKQKX20HpT9H7cxR7MYH+LzDDODDi+zPNYP8ipq6OhfnB1qeB8ym2\nJ+/6f8T2KL3jpTK688Bx//Jxbn/IZDw/Hbr8b9H7M6q7MMNe3wX+JLQ/V+/PQT/YkvH8LPoRWxxf\nxZT3uImZS/r3mMym58lBalsG9b+enwX+HJMa/AomMGkOJJpPAP+I+WyH01b1/hQRERERERERERER\nERERERERERERERERERER2ck/ATDNxtpAl5PZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f58cf4ba5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for parameter, scores in zip(parameter_values, all_scores):\n",
    "    n_scores = len(scores)\n",
    "    plt.plot([parameter] * n_scores, scores, '-o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f58cfb47b38>,\n",
       " <matplotlib.lines.Line2D at 0x7f58cfb47eb8>,\n",
       " <matplotlib.lines.Line2D at 0x7f58cfb4b080>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f58d0422dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(parameter_values, all_scores, 'bx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-20-53d2156a8105>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m         \u001b[0mestimator\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKNeighborsClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_neighbors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn_neighbors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m         \u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'accuracy'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m         \u001b[0mall_scores\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mn_neighbors\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mparameter\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mparameter_values\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python3.4/dist-packages/sklearn/cross_validation.py\u001b[0m in \u001b[0;36mcross_val_score\u001b[1;34m(estimator, X, y, scoring, cv, n_jobs, verbose, fit_params, score_func, pre_dispatch)\u001b[0m\n\u001b[0;32m   1149\u001b[0m                                               \u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1150\u001b[0m                                               fit_params)\n\u001b[1;32m-> 1151\u001b[1;33m                       for train, test in cv)\n\u001b[0m\u001b[0;32m   1152\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1153\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python3.4/dist-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m    650\u001b[0m                 \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mJOBLIB_SPAWNED_PROCESS\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'1'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    651\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 652\u001b[1;33m             \u001b[1;32mfor\u001b[0m \u001b[0mfunction\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    653\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    654\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python3.4/dist-packages/sklearn/cross_validation.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m   1149\u001b[0m                                               \u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1150\u001b[0m                                               fit_params)\n\u001b[1;32m-> 1151\u001b[1;33m                       for train, test in cv)\n\u001b[0m\u001b[0;32m   1152\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1153\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python3.4/dist-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mdelayed\u001b[1;34m(function, check_pickle)\u001b[0m\n\u001b[0;32m    118\u001b[0m     \u001b[1;31m# using with multiprocessing:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    119\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcheck_pickle\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 120\u001b[1;33m         \u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    121\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    122\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdelayed_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "all_scores = defaultdict(list)\n",
    "parameter_values = list(range(1, 21))  # Including 20\n",
    "for n_neighbors in parameter_values:\n",
    "    for i in range(100):\n",
    "        estimator = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
    "        scores = cross_val_score(estimator, X, y, scoring='accuracy', cv=10)\n",
    "        all_scores[n_neighbors].append(scores)\n",
    "for parameter in parameter_values:\n",
    "    scores = all_scores[parameter]\n",
    "    n_scores = len(scores)\n",
    "    plt.plot([parameter] * n_scores, scores, '-o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.plot(parameter_values, avg_scores, '-o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
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